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Spherical microaggregation: Anonymizing sparse vector spaces
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DOI：10.1016/j.cose.2014.11.005.png)
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Abstract
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Unstructured texts are a very popular data type and still widely unexplored in the privacy preserving data mining field. We consider the problem of providing public information about a set of confidential documents. To that end we have developed a method to protect a Vector Space Model (VSM), to make it public even if the documents it represents are private. This method is inspired by microaggregation, a popular protection method from statistical disclosure control, and adapted to work with sparse and high dimensional data sets. (C) 2014 Elsevier Ltd. All rights reserved.
Keywords:
Anonymization
Vector space
Privacy preserving
Data mining
Information loss
Sparse data
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